System

Abstract

An object of the system according to the embodiment is to generate a 3D image from a photograph without expertise and to output the 3D image by a snow printer.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a transmission unit. The reception unit receives a photograph from a user. The analysis unit analyzes the photograph received by the reception unit. The generating unit generates the 3D image based on a result of the analysis by the analyzing unit. The transmission unit transmits the 3D image generated by the generation unit to the 3D printer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of generating 3D image data from photographs and outputting it using a 3D printer was complicated and difficult to carry out without specialized knowledge.

[0005] The system according to the embodiment aims to generate 3D image data from photographs and output it using a 3D printer, even if the user does not have specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a transmitting unit. The receiving unit receives a photo from a user. The analyzing unit analyzes the photo received by the receiving unit. The generating unit generates 3D image data based on the analysis results by the analyzing unit. The transmitting unit transmits the 3D image data generated by the generating unit to a 3D printer. [Effects of the Invention]

[0007] The system according to the embodiment can generate 3D image data from photographs and output it using a 3D printer, even if the user does not have specialized knowledge. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is a smartphone app that uses generative AI to create 3D image data from several photos of an object taken with a smartphone. This system allows a user to take several photos of an object with a smartphone, and the generative AI analyzes these photos to generate 3D image data, which is then sent to a 3D printer to create a 3D copy. This allows users to easily create 3D image data and then print copies using a 3D printer. For example, the system can be used for a variety of purposes, such as creating models as a hobby or creating prototypes. Furthermore, since no specialized knowledge or tools are required, anyone can easily use it.

[0029] A 3D image generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a transmission unit. The receiving unit receives photos from a user. For example, photos taken by the user with a smartphone can be received in file formats such as JPEG, PNG, and RAW. The analysis unit analyzes the received photos. For example, the analysis unit can extract features of the photos using an image recognition algorithm. The generation unit generates 3D image data based on the results of the analysis by the analysis unit. For example, the generation unit can generate 3D image data in file formats such as STL and OBJ. The transmission unit transmits the 3D image data generated by the generation unit to a 3D printer. For example, the transmission unit can transmit the data using a communication method such as Wi-Fi or Bluetooth (registered trademark). This allows the 3D image generation system according to an embodiment to easily generate 3D image data from photos taken by a user with a smartphone and output the data using a 3D printer.

[0030] The analysis unit can extract features of the photo. For example, the analysis unit can extract the outline of the photo using edge detection. The analysis unit can also analyze the color distribution of the photo using a color histogram. The analysis unit can also use a shape analysis algorithm to extract shape features. By extracting the features of the photo, the accuracy of the 3D image data can be improved.

[0031] The generation unit can analyze the shape or texture of an object and construct a 3D model. For example, the generation unit can analyze the shape of the object using contour extraction. The generation unit can also analyze the shape using 3D scan data. The generation unit can also use a texture analysis algorithm to analyze the color or pattern of the surface. This allows for the generation of a more realistic 3D model by analyzing the shape and texture of the object.

[0032] The transmitter can send data directly from the smartphone to the 3D printer. The transmitter can send data from the smartphone to the 3D printer using, for example, Wi-Fi. The transmitter can also send data using Bluetooth. The transmitter can also send data using a USB cable. This allows you to easily create 3D copies by sending data directly from your smartphone to the 3D printer.

[0033] The reception unit can receive multiple photos taken from different angles. The reception unit can receive photos taken from different angles, such as the front, side, and back. The reception unit can also receive photos taken from an angle specified by the user. The reception unit can also receive multiple photos at once. This allows the entire image of an object to be captured by receiving photos taken from different angles.

[0034] The generation unit can generate 3D image data within the smartphone. The generation unit can generate 3D image data using, for example, a dedicated application within the smartphone. The generation unit can also generate 3D image data using hardware resources within the smartphone. The generation unit can also save 3D image data in storage within the smartphone. This eliminates the need to use specialized tools such as CAD software by creating 3D image data within the smartphone.

[0035] The reception unit can analyze the user's past photography history and select the optimal reception method. For example, the reception unit can prioritize and suggest photography methods that the user has frequently used in the past. The reception unit can also suggest the optimal photography angle and timing based on the user's past photography history. The reception unit can also analyze the user's past photography history and suggest the optimal photography method under specific conditions. In this way, the optimal reception method can be suggested by analyzing the user's past photography history.

[0036] The reception unit can filter photos based on the user's current project or area of ​​interest when receiving the photos. For example, the reception unit preferentially receives only photos related to the user's ongoing project. The reception unit can also filter related photos based on the user's area of ​​interest before receiving the photos. The reception unit can also exclude unnecessary photos based on the user's project or area of ​​interest before receiving the photos. In this way, by filtering photos based on the user's project or area of ​​interest, highly relevant photos can be preferentially received.

[0037] When accepting a photo, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user uses voice input, the acceptance unit accepts the photo based on a voice command. Also, if the user uses text input, the acceptance unit can accept the photo based on a text command. Also, if the user uses image input, the acceptance unit can accept the photo using image recognition technology. This improves user convenience by selecting the optimal acceptance means depending on the user's input method.

[0038] When accepting photos, the acceptance unit can preferentially accept photos that are highly relevant based on the user's geographical location information. For example, if the user is in a specific location, the acceptance unit preferentially accepts photos related to that location. The acceptance unit can also preferentially accept photos taken in locations close to the user's current location. The acceptance unit can also filter and accept photos that are highly relevant based on the user's geographical location information. In this way, highly relevant photos can be preferentially accepted by taking the user's geographical location information into consideration.

[0039] When accepting photos, the acceptance unit can analyze the user's social media activity and accept highly relevant photos. For example, the acceptance unit preferentially accepts photos that the user has shared on social media. The acceptance unit can also analyze the user's social media activity and accept related photos. The acceptance unit can also accept related photos by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related photos can be preferentially accepted.

[0040] The reception unit can customize the reception method based on the user's past feedback when receiving a photo. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also improve the reception procedure by reflecting the user's past feedback. The reception unit can also customize the optimal reception method under specific conditions based on the user's feedback. In this way, the optimal reception method can be suggested by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photo. For example, the analysis unit performs a detailed analysis on photos with high importance. The analysis unit can also perform a concise analysis on photos with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the photo. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the photo.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the photo. For example, the analysis unit applies a specific analysis algorithm to photos of natural scenery. The analysis unit can also apply a different analysis algorithm to photos of buildings. The analysis unit can also apply a dedicated analysis algorithm to photos of people. In this way, the accuracy of the analysis is improved by applying an analysis algorithm depending on the category of the photo.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis accuracy under specific conditions. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the photo was taken. For example, the analysis unit prioritizes analysis of the most recently taken photo. The analysis unit can also prioritize analysis of photos taken within a specific period. The analysis unit can also dynamically adjust the priority of analysis based on the time when the photo was taken. In this way, by determining the priority of analysis based on the time when the photo was taken, the most recent photo can be prioritized for analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the photos. For example, the analysis unit prioritizes the analysis of highly relevant photos. The analysis unit can also postpone the analysis of less relevant photos. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the photos. In this way, by adjusting the order of analysis based on the relevance of the photos, highly relevant photos can be prioritized in the analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0047] The generation unit may improve the accuracy of the 3D image data by taking into account the interrelationships between photos during generation. For example, the generation unit may analyze the interrelationships between multiple photos to improve the accuracy of the 3D image data. The generation unit may also adjust details of the 3D image data by taking into account the interrelationships between photos. The generation unit may also improve the overall accuracy of the 3D image data based on the interrelationships between photos. In this way, the accuracy of the 3D image data is improved by taking into account the interrelationships between photos.

[0048] The generation unit can generate 3D image data by taking into account the attribute information of the person who submitted the photo. The generation unit generates optimal 3D image data based on, for example, the age and gender of the person who submitted the photo. The generation unit can also generate customized 3D image data based on the occupation and hobbies of the person who submitted the photo. The generation unit can also generate optimal 3D image data under specific conditions based on the attribute information of the person who submitted the photo. This allows the generation of optimal 3D image data for the user by taking into account the attribute information of the person who submitted the photo.

[0049] The generation unit can weight the 3D image data based on the frequency of photo submissions during generation. For example, the generation unit prioritizes analysis of frequently submitted photos and weights the 3D image data. The generation unit can also prioritize analysis of less frequently submitted photos and weight the 3D image data. The generation unit can also dynamically adjust the weighting of the 3D image data based on the frequency of submission. This allows important photos to be prioritized in analysis by weighting based on the frequency of photo submission.

[0050] The generation unit may generate 3D image data taking into account the geographical distribution of photographs. For example, the generation unit may generate 3D image data based on geographical location information where the photographs were taken. The generation unit may also generate 3D image data by preferentially analyzing photographs taken in geographically close locations. The generation unit may also improve the accuracy of the 3D image data by taking into account the geographical distribution. As a result, the accuracy of the 3D image data is improved by taking into account the geographical distribution of the photographs.

[0051] The generating unit may improve the accuracy of the 3D image data by referring to literature related to the photograph during generation. For example, the generating unit may improve the accuracy of the 3D image data by referring to literature related to the photograph. The generating unit may also adjust details of the 3D image data based on information from the literature. The generating unit may also improve the overall accuracy of the 3D image data by referring to the literature. Thus, the accuracy of the 3D image data is improved by referring to the literature.

[0052] The generation unit can generate 3D image data taking into account the market value of the photo at the time of generation. For example, the generation unit can analyze photos with high market value first and generate 3D image data. Alternatively, the generation unit can analyze photos with low market value later and generate 3D image data. The generation unit can also dynamically adjust the method for generating 3D image data based on the market value. In this way, it is possible to generate high-value 3D image data by taking into account the market value of the photo.

[0053] The transmitting unit can adjust the level of detail of the transmission based on the importance of the 3D image data during transmission. For example, the transmitting unit prioritizes transmission of 3D image data with high importance. The transmitting unit can also transmit 3D image data with low importance later. The transmitting unit can also dynamically adjust the level of detail of the transmission according to the importance of the 3D image data. This allows for efficient transmission by adjusting the level of detail of the transmission according to the importance of the 3D image data.

[0054] The transmitter can apply different transmission algorithms depending on the category of the 3D image data during transmission. For example, the transmitter applies a dedicated transmission algorithm to 3D image data of a specific category. The transmitter can also select an optimal transmission algorithm depending on the category. The transmitter can also dynamically adjust the transmission algorithm based on the category of the 3D image data. This improves the accuracy of transmission by applying a transmission algorithm depending on the category of the 3D image data.

[0055] The transmitting unit can improve the accuracy of transmission by referring to the user's past transmission results when transmitting. The transmitting unit, for example, optimizes the transmission algorithm based on the user's past transmission results. The transmitting unit can also improve the accuracy of transmission by referring to the user's past transmission results. The transmitting unit can also analyze the user's past transmission results and improve the accuracy of transmission under specific conditions. In this way, the accuracy of transmission is improved by referring to the user's past transmission results.

[0056] The transmitting unit can determine the transmission priority based on the time when the 3D image data was created during transmission. For example, the transmitting unit can preferentially transmit the most recently created 3D image data. The transmitting unit can also preferentially transmit the 3D image data created within a specific period. The transmitting unit can also dynamically adjust the transmission priority based on the time when the 3D image data was created. Thus, by determining the transmission priority based on the time when the 3D image data was created, the latest data can be preferentially transmitted.

[0057] The transmitting unit can adjust the order of transmission based on the relevance of the 3D image data during transmission. For example, the transmitting unit prioritizes transmission of highly relevant 3D image data. The transmitting unit can also transmit less relevant 3D image data later. The transmitting unit can also dynamically adjust the order of transmission based on the relevance of the 3D image data. Thus, by adjusting the order of transmission based on the relevance of the 3D image data, highly relevant data can be transmitted preferentially.

[0058] The transmission unit can adjust the use of technical terms in the transmission according to the user's level of expertise during transmission. For example, if the user has technical expertise, the transmission unit can transmit using a lot of technical terms. Furthermore, if the user does not have technical expertise, the transmission unit can also transmit concise and easy-to-understand information. Furthermore, the transmission unit can dynamically adjust the use of technical terms in the transmission according to the user's level of expertise. This allows for transmission that is easy for the user to understand by adjusting the use of technical terms in the transmission according to the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past photography history and select the optimal reception method. For example, it can preferentially suggest photography methods that the user has frequently used in the past. The reception unit can also suggest the optimal photography angle and timing based on the user's past photography history. The reception unit can also analyze the user's past photography history and suggest the optimal photography method under specific conditions. In this way, the optimal reception method can be suggested by analyzing the user's past photography history.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the photo. For example, a detailed analysis is performed on a photo with a high level of importance. The analysis unit can also perform a brief analysis on a photo with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the photo. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the photo.

[0062] The generation unit can improve the accuracy of the 3D image data by taking into account the interrelationships between photos during generation. For example, the generation unit can analyze the interrelationships between multiple photos and improve the accuracy of the 3D image data. The generation unit can also adjust the details of the 3D image data by taking into account the interrelationships between photos. The generation unit can also improve the overall accuracy of the 3D image data based on the interrelationships between photos. In this way, the accuracy of the 3D image data is improved by taking into account the interrelationships between photos.

[0063] The transmitting unit can adjust the level of detail of the transmission based on the importance of the 3D image data during transmission. For example, the transmitting unit can transmit 3D image data with high importance first. The transmitting unit can also transmit 3D image data with low importance later. The transmitting unit can also dynamically adjust the level of detail of the transmission based on the importance of the 3D image data. This allows for efficient transmission by adjusting the level of detail of the transmission based on the importance of the 3D image data.

[0064] When accepting photos, the accepting unit can preferentially accept photos that are highly relevant based on the user's geographical location information. For example, if the user is in a specific location, photos related to that location are preferentially accepted. The accepting unit can also preferentially accept photos taken in locations close to the user's current location. The accepting unit can also filter and accept photos that are highly relevant based on the user's geographical location information. In this way, highly relevant photos can be preferentially accepted by taking the user's geographical location information into consideration.

[0065] The generation unit can generate 3D image data taking into account the market value of the photo at the time of generation. For example, photos with high market value can be analyzed first to generate 3D image data. The generation unit can also analyze photos with low market value later to generate 3D image data. The generation unit can also dynamically adjust the method for generating 3D image data based on the market value. In this way, high-value 3D image data can be generated by taking into account the market value of the photo.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives photos from the user. For example, photos taken by the user with a smartphone can be received in file formats such as JPEG, PNG, and RAW. Step 2: The analysis unit analyzes the received photo. For example, the analysis unit can use an image recognition algorithm to extract features of the photo. Step 3: The generator generates 3D image data based on the results of the analysis by the analyzer. For example, the generator can generate the 3D image data in a file format such as STL or OBJ. Step 4: The transmitting unit transmits the 3D image data generated by the generating unit to the 3D printer. For example, the transmitting unit can transmit the data using a communication means such as Wi-Fi or Bluetooth.

[0068] (Example 2) A system according to an embodiment of the present invention is a smartphone app that uses generative AI to create 3D image data from several photos of an object taken with a smartphone. This system allows a user to take several photos of an object with a smartphone, and the generative AI analyzes these photos to generate 3D image data, which is then sent to a 3D printer to create a 3D copy. This allows users to easily create 3D image data and then print copies using a 3D printer. For example, the system can be used for a variety of purposes, such as creating models as a hobby or creating prototypes. Furthermore, since no specialized knowledge or tools are required, anyone can easily use it.

[0069] A 3D image generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a transmission unit. The receiving unit receives photos from a user. For example, photos taken by the user with a smartphone can be received in file formats such as JPEG, PNG, and RAW. The analysis unit analyzes the received photos. For example, the analysis unit can extract features of the photos using an image recognition algorithm. The generation unit generates 3D image data based on the results of the analysis by the analysis unit. For example, the generation unit can generate 3D image data in file formats such as STL and OBJ. The transmission unit transmits the 3D image data generated by the generation unit to a 3D printer. For example, the transmission unit can transmit the data using a communication method such as Wi-Fi or Bluetooth. As a result, the 3D image generation system according to an embodiment allows a user to easily generate 3D image data from photos taken with a smartphone and output it using a 3D printer.

[0070] The analysis unit can extract features of the photo. For example, the analysis unit can extract the outline of the photo using edge detection. The analysis unit can also analyze the color distribution of the photo using a color histogram. The analysis unit can also use a shape analysis algorithm to extract shape features. By extracting the features of the photo, the accuracy of the 3D image data can be improved.

[0071] The generation unit can analyze the shape or texture of an object and construct a 3D model. For example, the generation unit can analyze the shape of the object using contour extraction. The generation unit can also analyze the shape using 3D scan data. The generation unit can also use a texture analysis algorithm to analyze the color or pattern of the surface. This allows for the generation of a more realistic 3D model by analyzing the shape and texture of the object.

[0072] The transmitter can send data directly from the smartphone to the 3D printer. The transmitter can send data from the smartphone to the 3D printer using, for example, Wi-Fi. The transmitter can also send data using Bluetooth. The transmitter can also send data using a USB cable. This allows you to easily create 3D copies by sending data directly from your smartphone to the 3D printer.

[0073] The reception unit can receive multiple photos taken from different angles. The reception unit can receive photos taken from different angles, such as the front, side, and back. The reception unit can also receive photos taken from an angle specified by the user. The reception unit can also receive multiple photos at once. This allows the entire image of an object to be captured by receiving photos taken from different angles.

[0074] The generation unit can generate 3D image data within the smartphone. The generation unit can generate 3D image data using, for example, a dedicated application within the smartphone. The generation unit can also generate 3D image data using hardware resources within the smartphone. The generation unit can also save 3D image data in storage within the smartphone. This eliminates the need to use specialized tools such as CAD software by creating 3D image data within the smartphone.

[0075] The reception unit can estimate the user's emotions and adjust the timing of photo acceptance based on the emotions. For example, if the user is relaxed, the reception unit adjusts the timing of photo acceptance slowly so that the user can take photos without rushing. Furthermore, if the user is in a hurry, the reception unit adjusts the timing of photo acceptance quickly so that the user can complete the photo shoot quickly. Furthermore, if the user is excited, the reception unit adjusts the timing of photo acceptance appropriately so that the user can take photos calmly. In this way, by adjusting the timing of photo acceptance according to the user's emotions, photos can be accepted at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can analyze the user's past photography history and select the optimal reception method. For example, the reception unit can prioritize and suggest photography methods that the user has frequently used in the past. The reception unit can also suggest the optimal photography angle and timing based on the user's past photography history. The reception unit can also analyze the user's past photography history and suggest the optimal photography method under specific conditions. In this way, the optimal reception method can be suggested by analyzing the user's past photography history.

[0077] The reception unit can filter photos based on the user's current project or area of ​​interest when receiving the photos. For example, the reception unit preferentially receives only photos related to the user's ongoing project. The reception unit can also filter related photos based on the user's area of ​​interest before receiving the photos. The reception unit can also exclude unnecessary photos based on the user's project or area of ​​interest before receiving the photos. In this way, by filtering photos based on the user's project or area of ​​interest, highly relevant photos can be preferentially received.

[0078] When accepting a photo, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user uses voice input, the acceptance unit accepts the photo based on a voice command. Also, if the user uses text input, the acceptance unit can accept the photo based on a text command. Also, if the user uses image input, the acceptance unit can accept the photo using image recognition technology. This improves user convenience by selecting the optimal acceptance means depending on the user's input method.

[0079] The reception unit can estimate the user's emotions and determine the priority of photos to be received based on the emotions. For example, when the user is relaxed, the reception unit can prioritize receiving photos with high importance. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving photos that can be processed quickly. Furthermore, when the user is excited, the reception unit can prioritize receiving photos related to the emotion. In this way, by determining the priority of photos according to the user's emotions, important photos can be received preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When accepting photos, the acceptance unit can preferentially accept photos that are highly relevant based on the user's geographical location information. For example, if the user is in a specific location, the acceptance unit preferentially accepts photos related to that location. The acceptance unit can also preferentially accept photos taken in locations close to the user's current location. The acceptance unit can also filter and accept photos that are highly relevant based on the user's geographical location information. In this way, highly relevant photos can be preferentially accepted by taking the user's geographical location information into consideration.

[0081] When accepting photos, the acceptance unit can analyze the user's social media activity and accept highly relevant photos. For example, the acceptance unit preferentially accepts photos that the user has shared on social media. The acceptance unit can also analyze the user's social media activity and accept related photos. The acceptance unit can also accept related photos by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related photos can be preferentially accepted.

[0082] The reception unit can customize the reception method based on the user's past feedback when receiving a photo. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also improve the reception procedure by reflecting the user's past feedback. The reception unit can also customize the optimal reception method under specific conditions based on the user's feedback. In this way, the optimal reception method can be suggested by reflecting the user's past feedback.

[0083] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is excited, the analysis unit can also provide visually appealing analysis results. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photo. For example, the analysis unit performs a detailed analysis on photos with high importance. The analysis unit can also perform a concise analysis on photos with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the photo. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the photo.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the photo. For example, the analysis unit applies a specific analysis algorithm to photos of natural scenery. The analysis unit can also apply a different analysis algorithm to photos of buildings. The analysis unit can also apply a dedicated analysis algorithm to photos of people. In this way, the accuracy of the analysis is improved by applying an analysis algorithm depending on the category of the photo.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis accuracy under specific conditions. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually appealing analysis result if the user is excited. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time when the photo was taken. For example, the analysis unit prioritizes analysis of the most recently taken photo. The analysis unit can also prioritize analysis of photos taken within a specific period. The analysis unit can also dynamically adjust the priority of analysis based on the time when the photo was taken. In this way, by determining the priority of analysis based on the time when the photo was taken, the most recent photo can be prioritized for analysis.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the photos. For example, the analysis unit prioritizes the analysis of highly relevant photos. The analysis unit can also postpone the analysis of less relevant photos. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the photos. In this way, by adjusting the order of analysis based on the relevance of the photos, highly relevant photos can be prioritized in the analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0091] The generation unit can estimate the user's emotions and adjust the method for generating 3D image data based on the emotions. For example, if the user is relaxed, the generation unit generates 3D image data that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate 3D image data that can be generated in the shortest time. Furthermore, if the user is excited, the generation unit can generate 3D image data that adds visually stimulating effects. Thus, by adjusting the method for generating 3D image data according to the user's emotions, optimal 3D image data for the user can be generated. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The generation unit may improve the accuracy of the 3D image data by taking into account the interrelationships between photos during generation. For example, the generation unit may analyze the interrelationships between multiple photos to improve the accuracy of the 3D image data. The generation unit may also adjust details of the 3D image data by taking into account the interrelationships between photos. The generation unit may also improve the overall accuracy of the 3D image data based on the interrelationships between photos. In this way, the accuracy of the 3D image data is improved by taking into account the interrelationships between photos.

[0093] The generation unit can generate 3D image data by taking into account the attribute information of the person who submitted the photo. The generation unit generates optimal 3D image data based on, for example, the age and gender of the person who submitted the photo. The generation unit can also generate customized 3D image data based on the occupation and hobbies of the person who submitted the photo. The generation unit can also generate optimal 3D image data under specific conditions based on the attribute information of the person who submitted the photo. This allows the generation of optimal 3D image data for the user by taking into account the attribute information of the person who submitted the photo.

[0094] The generation unit can weight the 3D image data based on the frequency of photo submissions during generation. For example, the generation unit prioritizes analysis of frequently submitted photos and weights the 3D image data. The generation unit can also prioritize analysis of less frequently submitted photos and weight the 3D image data. The generation unit can also dynamically adjust the weighting of the 3D image data based on the frequency of submission. This allows important photos to be prioritized in analysis by weighting based on the frequency of photo submission.

[0095] The generation unit can estimate the user's emotions and adjust the display method of the 3D image data based on the emotions. For example, if the user is relaxed, the generation unit can provide 3D image data displayed at a leisurely pace. If the user is in a hurry, the generation unit can also provide 3D image data displayed quickly. If the user is excited, the generation unit can also provide 3D image data with a visually stimulating effect. This allows for optimal display for the user by adjusting the display method of the 3D image data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The generation unit may generate 3D image data taking into account the geographical distribution of photographs. For example, the generation unit may generate 3D image data based on geographical location information where the photographs were taken. The generation unit may also generate 3D image data by preferentially analyzing photographs taken in geographically close locations. The generation unit may also improve the accuracy of the 3D image data by taking into account the geographical distribution. As a result, the accuracy of the 3D image data is improved by taking into account the geographical distribution of the photographs.

[0097] The generating unit may improve the accuracy of the 3D image data by referring to literature related to the photograph during generation. For example, the generating unit may improve the accuracy of the 3D image data by referring to literature related to the photograph. The generating unit may also adjust details of the 3D image data based on information from the literature. The generating unit may also improve the overall accuracy of the 3D image data by referring to the literature. Thus, the accuracy of the 3D image data is improved by referring to the literature.

[0098] The generation unit can generate 3D image data taking into account the market value of the photo at the time of generation. For example, the generation unit can analyze photos with high market value first and generate 3D image data. Alternatively, the generation unit can analyze photos with low market value later and generate 3D image data. The generation unit can also dynamically adjust the method for generating 3D image data based on the market value. In this way, it is possible to generate high-value 3D image data by taking into account the market value of the photo.

[0099] The transmission unit can estimate the user's emotions and adjust the transmission method of the 3D image data based on the emotions. For example, if the user is relaxed, the transmission unit can transmit the 3D image data at a leisurely pace. Furthermore, if the user is in a hurry, the transmission unit can transmit the 3D image data quickly. Furthermore, if the user is excited, the transmission unit can transmit the 3D image data with a visually stimulating effect. This allows optimal transmission for the user by adjusting the transmission method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The transmitting unit can adjust the level of detail of the transmission based on the importance of the 3D image data during transmission. For example, the transmitting unit prioritizes transmission of 3D image data with high importance. The transmitting unit can also transmit 3D image data with low importance later. The transmitting unit can also dynamically adjust the level of detail of the transmission according to the importance of the 3D image data. This allows for efficient transmission by adjusting the level of detail of the transmission according to the importance of the 3D image data.

[0101] The transmitter can apply different transmission algorithms depending on the category of the 3D image data during transmission. For example, the transmitter applies a dedicated transmission algorithm to 3D image data of a specific category. The transmitter can also select an optimal transmission algorithm depending on the category. The transmitter can also dynamically adjust the transmission algorithm based on the category of the 3D image data. This improves the accuracy of transmission by applying a transmission algorithm depending on the category of the 3D image data.

[0102] The transmitting unit can improve the accuracy of transmission by referring to the user's past transmission results when transmitting. The transmitting unit, for example, optimizes the transmission algorithm based on the user's past transmission results. The transmitting unit can also improve the accuracy of transmission by referring to the user's past transmission results. The transmitting unit can also analyze the user's past transmission results and improve the accuracy of transmission under specific conditions. In this way, the accuracy of transmission is improved by referring to the user's past transmission results.

[0103] The transmission unit can estimate the user's emotions and adjust the length of the transmission based on the emotions. For example, if the user is in a hurry, the transmission unit can send a short, to-the-point transmission. If the user is relaxed, the transmission unit can also send a transmission containing detailed information. If the user is excited, the transmission unit can also send a transmission with a visually stimulating effect. This allows the transmission to be optimized for the user by adjusting the length of the transmission according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] The transmitting unit can determine the transmission priority based on the time when the 3D image data was created during transmission. For example, the transmitting unit can preferentially transmit the most recently created 3D image data. The transmitting unit can also preferentially transmit the 3D image data created within a specific period. The transmitting unit can also dynamically adjust the transmission priority based on the time when the 3D image data was created. Thus, by determining the transmission priority based on the time when the 3D image data was created, the latest data can be preferentially transmitted.

[0105] The transmitting unit can adjust the order of transmission based on the relevance of the 3D image data during transmission. For example, the transmitting unit prioritizes transmission of highly relevant 3D image data. The transmitting unit can also transmit less relevant 3D image data later. The transmitting unit can also dynamically adjust the order of transmission based on the relevance of the 3D image data. Thus, by adjusting the order of transmission based on the relevance of the 3D image data, highly relevant data can be transmitted preferentially.

[0106] The transmission unit can adjust the use of technical terms in the transmission according to the user's level of expertise during transmission. For example, if the user has technical expertise, the transmission unit can transmit using a lot of technical terms. Furthermore, if the user does not have technical expertise, the transmission unit can also transmit concise and easy-to-understand information. Furthermore, the transmission unit can dynamically adjust the use of technical terms in the transmission according to the user's level of expertise. This allows for transmission that is easy for the user to understand by adjusting the use of technical terms in the transmission according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and transmission unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives photos taken by a user with a smartphone. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received photos. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates 3D image data based on the analysis results. For example, the transmission unit is realized by the communication I / F 44 of the smart device 14 and transmits the generated 3D image data to a 3D printer. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a photo taken by a user with a smartphone. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received photo. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates 3D image data based on the analysis results. For example, the transmission unit is realized by the communication I / F 44 of the smart glasses 214 and transmits the generated 3D image data to a 3D printer. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives photos taken by a user with a smartphone. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received photos. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates 3D image data based on the analysis results. For example, the transmission unit is realized by the communication I / F 44 of the headset type terminal 314 and transmits the generated 3D image data to a 3D printer. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives photos taken by a user with a smartphone. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received photos. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates 3D image data based on the analysis results. For example, the transmission unit is realized by the communication I / F 44 of the robot 414 and transmits the generated 3D image data to a 3D printer.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's past photography history and select the optimal reception method. For example, it can preferentially suggest photography methods that the user has frequently used in the past. The reception unit can also suggest the optimal photography angle and timing based on the user's past photography history. The reception unit can also analyze the user's past photography history and suggest the optimal photography method under specific conditions. In this way, the optimal reception method can be suggested by analyzing the user's past photography history.

[0109] The analysis unit can adjust the level of detail of the analysis based on the importance of the photo. For example, a detailed analysis is performed on a photo with a high level of importance. The analysis unit can also perform a brief analysis on a photo with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the photo. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the photo.

[0110] The generation unit can improve the accuracy of the 3D image data by taking into account the interrelationships between photos during generation. For example, the generation unit can analyze the interrelationships between multiple photos and improve the accuracy of the 3D image data. The generation unit can also adjust the details of the 3D image data by taking into account the interrelationships between photos. The generation unit can also improve the overall accuracy of the 3D image data based on the interrelationships between photos. In this way, the accuracy of the 3D image data is improved by taking into account the interrelationships between photos.

[0111] The transmitting unit can adjust the level of detail of the transmission based on the importance of the 3D image data during transmission. For example, the transmitting unit can transmit 3D image data with high importance first. The transmitting unit can also transmit 3D image data with low importance later. The transmitting unit can also dynamically adjust the level of detail of the transmission based on the importance of the 3D image data. This allows for efficient transmission by adjusting the level of detail of the transmission based on the importance of the 3D image data.

[0112] When accepting photos, the accepting unit can preferentially accept photos that are highly relevant based on the user's geographical location information. For example, if the user is in a specific location, photos related to that location are preferentially accepted. The accepting unit can also preferentially accept photos taken in locations close to the user's current location. The accepting unit can also filter and accept photos that are highly relevant based on the user's geographical location information. In this way, highly relevant photos can be preferentially accepted by taking the user's geographical location information into consideration.

[0113] The reception unit can estimate the user's emotions and determine the priority of photos to be received based on the emotions. For example, if the user is relaxed, it can prioritize receiving photos with high importance. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving photos that can be processed quickly. Furthermore, if the user is excited, the reception unit can prioritize receiving photos related to the emotion. In this way, by determining the priority of photos according to the user's emotions, important photos can be received preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is excited, the analysis unit can also provide visually appealing analysis results. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] The generation unit can estimate the user's emotions and adjust the method for generating 3D image data based on the emotions. For example, if the user is relaxed, the generation unit can generate 3D image data that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate 3D image data that can be generated in the shortest time. If the user is excited, the generation unit can generate 3D image data that adds visually stimulating effects. By adjusting the method for generating 3D image data according to the user's emotions, optimal 3D image data can be generated for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The transmission unit can estimate the user's emotions and adjust the transmission method of the 3D image data based on the emotions. For example, if the user is relaxed, the transmission unit can transmit the 3D image data at a leisurely pace. If the user is in a hurry, the transmission unit can transmit the 3D image data quickly. If the user is excited, the transmission unit can transmit the 3D image data with a visually stimulating effect. This allows optimal transmission for the user by adjusting the transmission method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The generation unit can generate 3D image data taking into account the market value of the photo at the time of generation. For example, photos with high market value can be analyzed first to generate 3D image data. The generation unit can also analyze photos with low market value later to generate 3D image data. The generation unit can also dynamically adjust the method for generating 3D image data based on the market value. In this way, high-value 3D image data can be generated by taking into account the market value of the photo.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives photos from the user. For example, photos taken by the user with a smartphone can be received in file formats such as JPEG, PNG, and RAW. Step 2: The analysis unit analyzes the received photo. For example, the analysis unit can use an image recognition algorithm to extract features of the photo. Step 3: The generator generates 3D image data based on the results of the analysis by the analyzer. For example, the generator can generate the 3D image data in a file format such as STL or OBJ. Step 4: The transmitting unit transmits the 3D image data generated by the generating unit to the 3D printer. For example, the transmitting unit can transmit the data using a communication means such as Wi-Fi or Bluetooth.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a photo from a user; an analysis unit that analyzes the photograph accepted by the acceptance unit; a generation unit that generates 3D image data based on the results of the analysis by the analysis unit; a transmission unit that transmits the 3D image data generated by the generation unit to a 3D printer. A system characterized by:

2. The analysis unit Extracting Photo Features 2. The system of claim 1.

3. The generation unit Analyze the shape or texture of an object and build a 3D model 2. The system of claim 1.

4. The transmission unit Send data directly from your smartphone to your 3D printer 2. The system of claim 1.

5. The reception unit Accepts multiple photos taken from different angles 2. The system of claim 1.

6. The generation unit Generate 3D image data on your smartphone 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of photo acceptance based on those emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past photo-taking history and select the optimal reception method 2. The system of claim 1.

Images